Time to an event of interest over a lifetime is a central measure of the clinical benefit of an intervention used in a health technology assessment (HTA). Within the same trial multiple end-points may also be considered. For example, overall and progression-free survival time for different drugs in oncology studies. A common challenge is when an intervention is only effective for some proportion of the population who are not clinically identifiable. Therefore, latent group membership as well as separate survival models for groups identified need to be estimated. However, follow-up in trials may be relatively short leading to substantial censoring. We present a general Bayesian hierarchical framework that can handle this complexity by exploiting the similarity of cure fractions between end-points; accounting for the correlation between them and improving the extrapolation beyond the observed data. Assuming exchangeability between cure fractions facilitates the borrowing of information between end-points. We show the benefits of using our approach with a motivating example, the CheckMate 067 phase 3 trial consisting of patients with metastatic melanoma treated with first line therapy.

翻译：在健康技术评估（HTA）中，个体一生中发生感兴趣事件的时间是衡量干预措施临床获益的核心指标。同一项试验中可能考虑多个终点指标。例如，肿瘤学研究中不同药物的总生存期和无进展生存期。一个常见的挑战是，当干预措施仅对部分临床上无法识别的群体有效时，需要估计潜在群体归属以及不同群体的独立生存模型。然而，试验的随访期可能相对较短，导致大量删失。我们提出一个通用的贝叶斯分层框架，通过利用各终点之间治愈比例的相似性来处理这一复杂性；考虑它们之间的相关性，并改进对观察数据之外的趋势外推。假设治愈比例之间具有可交换性，有助于实现各终点之间的信息借力。我们以CheckMate 067 III期试验（包含接受一线治疗的转移性黑色素瘤患者）作为实例，展示了我们方法的优势。